Driver Input Parameters TRIZ Cases

When braking interrupts cruise control at a stop, the system proposes restart and resumes hold automatically when brake input is released.

Controlled longitudinal acceleration offsets lateral transient forces to stabilize occupant posture during lane changes without complex seat mechanisms.

When a driver becomes unable to continue, the vehicle stops and shows rescue images so occupants can act quickly without unsafe movement.

Turn-signal-triggered overtaking assist suppresses distance and speed control during rider-independent acceleration and blocks use on single-lane roads.

A driver-enabled switch lets an autonomous vehicle cross solid lines for lane changes, balancing rule compliance with emergency avoidance.

Pedal force is increased from vehicle deceleration data to simulate brake fade, helping drivers detect runout earlier and avoid excess heat.

Sensor fusion compares driver intent with gear setting and intervenes in braking or acceleration to prevent low-speed collision errors.

When driver contribution drops, the seat shifts from an autonomous posture to an intermediate position to speed safe manual takeover.

Adaptive deceleration based on driver dozing level helps avoid secondary collisions without triggering unnecessary braking after a light collision.

Sensor analysis of driver head position and movement detects inattentive autonomous vehicle monitors and triggers timely interventions.

Future path prediction lets ACC handle lane changes and road splits while maintaining safe distance, speed limits, and smoother speed control.

Gradual acceleration-rate limits curb sudden acceleration during autonomous-to-manual takeover while preserving stable vehicle response.

Partial steering reference acquisition enables earlier autonomous driving activation while maintaining stability through yaw-rate checks and driver hold requests.

An avatar-based vehicle message turns driving behavior into personality-driven feedback that helps drivers reflect objectively and drive more safely.

Seat position and backrest angle are monitored to limit unsafe adjustment and trigger timely manual takeover during autonomous driving.

Standardized longitudinal and lateral request arbitration simplifies vehicle actuator control when multiple driving assistance applications issue commands.

Image-based stop control lets an abnormal-driver vehicle pass intersections or crossings before stopping, reducing road obstruction.

A saturated steering setpoint limits direction-change speed so vehicles avoid obstacles while preserving stability and driver takeover.

Using vehicle dynamics modeling and optimization, this case generates control directives tailored to real driving patterns and environments.

Preloaded praise and criticism are output during driving based on vehicle information, giving timely feedback without overwhelming the driver.

Gradual torque limiting based on vehicle state smooths motorcycle speed control engagement and release, reducing abrupt acceleration or deceleration.

Real-time equivalent inertia calibration improves wheel slip control in multi-drive vehicles without reference speed, boosting stability in transient driving.

Roll angle transitions from fixed mobile sensors enable real-time boarding and alighting detection with fewer false positives in ride services.

Wheel height is adjusted from steering angle and torque data to counter vehicle pull during straight driving and reduce driver intervention.

Manual intervention can overfill a PID controller’s integral term; freezing or resetting it enables smoother handover and faster automated recovery.

After emergency braking, the controller stores vehicle events and enters minimum risk maneuver mode until sensor or system faults are resolved.

During transmission shifts, spark retard and selective cylinder shutdown reduce flare-driven torque swings while helping prevent engine misfires.

Separate wireless chipsets and virtual machines route smartphone and vehicle audio to multiple headsets while preserving alerts and personal media.

Dynamic obstacle range switching by vehicle travel direction cuts false detections, unnecessary braking, and parking delays.

Seat position and backrest angle thresholds govern autonomous mode, seat adjustment limits, and takeover timing to reduce unsafe handover delays.

Continuous switch actuation creates a deliberate transition to autonomous driving while blocking manual input interference and allowing safe disengagement.

Closed-loop shift and torque compensation reduces harsh brake-to-steer deceleration while preserving lateral control and a natural driving feel.

Optical pulse sensing in the steering wheel detects driver pulseless events in real time, then alerts occupants and brings the vehicle to a controlled stop.

Combining steering behavior and lane position with adaptive sensitivity improves driver drowsiness alerts and cuts false alarms.

Combining steering behavior and lane position data enables personalized drowsiness detection with fewer false alarms under varying driving conditions.

Passenger feedback updates g-g plot acceleration limits so autonomous driving stays within a comfort-adapted dynamic envelope.

Brake-pedal feedback updates speed-based one-pedal deceleration levels so slowing behavior stays consistent with driver intent.

Radar tracks passenger reach toward seatbelts and door handles through blankets or toys, enabling alerts that counter unsafe in-vehicle actions.

Pre-movement millimeter-wave cabin scans detect excess or unsafe occupant positions and can stop vehicle motion until seating is safe.

Driver-state sensing escalates from warnings to steering hold requests, keeping hands-off driving safer when attention drops.

Multi-period steering angle speed checks with adaptive thresholds improve abnormality detection and trigger safer vehicle mode switching.

Dynamic takeover thresholds use driver readiness detection to prevent unintended manual switching during automatic driving control.

Passenger data and route time are combined to build taxi playlists that fit ride duration and improve ad relevance and engagement.

Predictive AR overlays a virtual future vehicle on the driver's view, making risky trajectories easier to grasp before hazards fully develop.

Risk-based filtering highlights only high-threat nearby objects, reducing driver overload in complex traffic and improving safety confirmation.

Maintains braking after an abnormal driver stop and blocks release from accelerator input, preventing unintended vehicle movement during rescue.